Curvature based method for selecting features from an electrophysiologic signals for purpose of complex identification and classification

ABSTRACT

A method for curvature based complex identification and classification comprises sensing a cardiac signal and computing curvatures at sample points on the sensed cardiac signal. Then to extract features from the computed curvatures, and compare the extracted features with a set of predetermined templates, and then to classify the sensed cardiac signal based on the outcome of the comparison.

The present invention relates generally to cardiac rhythm managementsystems, and more particularly, it pertains to a system and method ofclassification and detection of cardiac signals.

BACKGROUND OF THE INVENTION

Analysis of cardiac signals, which is routinely performed inelectrocardiography is generally based on visual inspection to quantifyor qualify wave morphology for the purpose of identifying andclassifying abnormal patterns. Certain morphological characteristics ofcommonly recorded signals have high diagnostic value. The shape andinter arrival times of R-waves recorded in the electrocardiogramgenerally provide a wealth of information about the state of the heart.Accordingly, automated approaches for identifying and classifyingabnormalities in signals such as cardiac signals have sought to use asignal's significant morphologic characteristics.

However, given the wide diversity of possible shapes for cardiacsignals, it is usually not possible for an automatic approach toidentify significant characteristics that can be used for unambiguousclassification. Rather, automated classification approaches generallycompare the entire morphological shape of a signal with the shape ofsimilar signals with known abnormalities but without particular regardto the specific characteristics that the signals contain. Alternatively,automated classification approaches restrict the automated examinationonly to those signals which are essentially normal and use detailedmetrics (for example QRS width, QT interval or ST segment amplitude) ofthe essentially normal morphology for classifying abnormalities.

Despite its importance in the analysis of biologic signals, theautomated and accurate identification and quantification of thesignificant morphological characteristics (for example turns, peaks,knees, inflection points, and the like) in any cardiac signal (bothabnormal as well as normal) is still in a developing stage. Existingmethods have used the concept of sharpness (for example to detectR-waves) but have had limited success. This is due in part to the overlysimplistic mathematical treatment this concept has received, asreflected in the rudimentary algorithms used for these measurements.Most of the current detection methods rely on three point interpolationsto measure sharpness. The simplest and most commonly used methods formeasuring peaks of R-waves are based upon Taylor-series approximationsto estimate the second derivative of the sensed signal. This formulautilizes highly local information (the point at the peak and its twoclose neighbors) ignoring nearby points which may contribute to signalpeak. Other popular approaches utilize less local data, such as the peakand two adjacent extrema. All of these methods, which rely onthree-point estimates of sharpness, may produce inaccurate estimates, ifwaveforms are complex or are contaminated with noise. Thus, a needexists for automated identification and classification of peaks, knees,inflection points, and the like in sensed cardiac signals that takesinto account wave scale and complexity that can yield a more accurateestimate of peaks for identifying and classifying abnormalities incardiac signals.

SUMMARY OF THE INVENTION

The present subject matter provides a curvature based method ofselecting features from electrophysiologic signals for purpose ofcomplex identification and classification. According to one aspect ofthe present subject matter, this is accomplished by sensing a cardiacsignal (sensing the cardiac signal includes sensing complexescontinuously on a real-time basis) and computing curvatures on a samplepoint-by-sample point basis (X₁, X₂, X₃, . . . X_(I)) on the sensedcardiac signal on a continuous basis. In one embodiment, the curvatureat the sample points X₁, X₂, X₃, . . . X_(I) are computed by fitting acubic least square error curve (using N number of sample points) to thesensed cardiac signal. In this embodiment, N is an odd number, and thesample point (where the curvature is computed) is at a mid point of theN number of sample points.

Also on a continuous basis, features of significant interest areextracted from the computed curvatures. In some embodiments, this isaccomplished by comparing the 30 computed curvatures at the samplepoints X₁, X₂, X₃, . . . X_(I) to a set of predetermined thresholdvalues. In some embodiments, the features are extracted based oncomputing features such as a time when the feature occurs, an amplitudeof the feature, and other similar features at each of the sample pointsand comparing them to a set of threshold values.

Also on a continuous basis a set of features associated with a firstcomplex in the sensed cardiac signal are identified and separated fromthe continuously computed and extracted features upon detecting a secondsubsequent complex. The second subsequent complex is a complex that isadjacent to the first complex and occurs substantially immediately afterthe first complex. This process of identifying and separating extractedfeatures repeats itself from one sensed complex to another subsequentsensed complex on a real time basis. One reason for identifying andseparating the set of features associated with the first complex is toprevent the features associated with the first complex from mixing withthe features associated the second subsequent complex. Separating thefeatures associated with first complex aids in classifying the sensedfirst complex.

Next, the process includes identifying a fiducial feature from theseparated set of extracted features associated with the first complexand aligning the separated set of features with respect to theidentified fiducial feature. In one embodiment, fiducial feature isidentified based on comparing the times when each of the separated setof features occur with a time when a complex associated with theseparated set of features is detected on the sensed cardiac signal, andselecting a feature from the separated set of features that is closestin time to the time when the complex associated with the separated setof features was detected. One reason for using a time when a complex isdetected (such as R wave) in identifying the fiducial feature, isbecause the detection of a complex in a sensed cardiac signal isgenerally more reliable and consistent.

Next, the process includes aligning the separated set of features aroundthe identified fiducial feature. Aligning the separated set of featuresaround the identified fiducial feature aids in normalizing each of theseparated set of feature around a datum such as the associated detectedcomplex, and further aids in comparing the separated set of featureswith a set of predetermined templates.

Next the process includes comparing the aligned set of features to a setof predetermined templates to classify the associated complex. In someembodiments, the predetermined templates are a set of identifiedcomplexes associated with known cardiac arrhythmias that would assist incomparing and classifying the extracted set of features. In someembodiments, a therapy is provided to the heart based on the outcome ofthe classification. The above described process repeats itself on acontinuous basis for a real-time classification of complexes from thesensed cardiac signal.

Other aspects of the invention will be apparent on reading the followingdetailed description of the invention and viewing the drawings that forma part thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a cardiac signal according to one aspect of the presentinvention.

FIG. 2 is a curvature figure derived from the cardiac signal ofaccording to one aspect of the present invention.

FIGS. 3 and 3B illustrate separating and aligning features derived fromthe curvature figure of FIG. 2 according to one aspect of the presentinvention.

FIG. 4 is a flow diagram illustrating generally one embodiment ofoperation of the present su matter.

FIG. 5 is a schematic/block diagram illustrating generally oneembodiment of portions of a cardiac rhythm management system of thepresent subject matter.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that the embodiments may be combined, or that otherembodiments may be utilized and that structural, logical and electricalchanges may be made without departing from the spirit and scope of thepresent invention. The following detailed description is, therefore, notto be taken in a limiting sense, and the scope of the present inventionis defined by the appended claims and their equivalents.

Analysis of cardiac signals, which is routinely performed inelectrocardiography is generally based on visual inspection to quantifyor qualify wave morphology for the purpose of identifying andclassifying abnormal patterns. Certain morphological characteristics ofcommonly recorded signals have high diagnostic value. The shape andinter arrival times of R-waves recorded in the electrocardiogramgenerally provide a wealth of information about the state of the heart.Accordingly, automated approaches for identifying and classifyingabnormalities in signals such as cardiac signals have sought to use asignal's significant morphologic characteristics.

However, given the wide diversity of possible shapes for cardiacsignals, it is usually not possible for an automatic approach toidentify significant characteristics that can be used for unambiguousclassification. Rather, automated classification approaches generallycompare the entire morphological shape of a signal with the shape ofsimilar signals with known abnormalities but without particular regardto the specific characteristics that the signals contain. Alternatively,automated classification approaches restrict the automated examinationonly to those signals which are essentially normal and use detailedmetrics (for example QRS width, QT interval or ST segment amplitude) ofthe essentially normal morphology for classifying abnormalities.

Despite its importance in the analysis of biologic signals, theautomated and accurate identification and quantification of thesignificant morphological characteristics (for example turns, peaks,knees, inflection points, and the like) in any cardiac signal (bothabnormal as well as normal) is still in a developing stage. Existingmethods have used the concept of sharpness (for example to detectR-waves) but have had limited success. This is due in part to the overlysimplistic mathematical treatment this concept has received, asreflected in the rudimentary algorithms used for these measurements.Most of the current detection methods rely on three point interpolationsto measure sharpness, may produce inaccurate estimates, if waveforms arecomplex or are contaminated with noise.

The present subject matter provides, among other things, a system ofclassifying a cardiac signal by computing a curvature versus time signalfrom a sensed cardiac signal and comparing the computed curvature versustime signal with a set of predetermined templates. The present systemalso provides an automated identification and classification of peaks,knees, inflection points, and the like in cardiac signals that takesinto account wave scale and complexity that can yield a more accurateclassification of cardiac signals.

The first step in the process includes computing continuously acurvature versus time signal from a sensed cardiac signal on a real-timebasis. The process also includes extracting features (features aregenerally significant points of interest on the sensed cardiac signal)continuously on a real-time basis from the computed curvature versustime signal. Next, the process includes continuously separating a set offeatures associated with a first complex upon detecting a secondsubsequent complex so that each individual complex can be identified andclassified on a real-time basis. Then the process includes aligning andclassifying the extracted and separated set of features by comparing theseparated set of features with a set of predetermined templates. Otheraspects of the invention will be apparent on reading the followingdetailed description of the invention and viewing the drawings that forma part thereof.

Referring now to FIG. 1, there is one embodiment of the first step inthe process of sensing a cardiac signal 100 according to the presentinvention. Shown in FIG. 1 are detection of heart beats associated withfirst and second complexes 110 and 120 at times ‘t₁’ and ‘t₂’ associatedwith the sensed cardiac signal 100 on a real-time basis on a time line130. Also, shown on the time line 130 are the various sample points X₁,X₂, X₃, . . . X_(I) used in computing a curvature versus time signal onthe first complex 110. In the example embodiment shown in FIG. 1, thesample points can be some fixed interval of time, such as 2, 4, 6 . . .milliseconds, at which curvatures are computed on the sensed cardiacsignal. Also shown in this embodiment, is the detection of the complexes(110, 120, and so on) on a continuous basis on the sensed cardiac signal100.

Referring now to FIG. 2, there is shown one embodiment of generating acurvature versus time signal 200 on a real-time basis derived from thesensed cardiac signal 100 shown in FIG. 1 according to the presentinvention. FIG. 2 is also on the same time line 130 as in FIG. 1.Curvature FIG. 200 shown in FIG. 2 is a computed curvature (expressed intime) versus time signal generated continuously by computing curvaturesat the sample points X₁, X₂, X₃, . . . X_(I) on the sensed cardiacsignal 100 shown in FIG. 1. Curvature FIGS. 210 and 220 are computedcurvature versus time signals associated with first and second complexes110 and 120 shown in FIG. 1, respectively. In this example embodiment,the curvature versus time signal is computed by overcoming thedimensionality of the curvature by changing the dimensionality of thecurvature to time. The reason for overcoming the dimensionality of thecurvature is to convert the time versus voltage signal, which is timeversus curvature, to a time versus time signal to facilitate easiercomputation of curvature (for example, when a curve exists inlength-length space, the quantity has dimension of length and istraditionally called the radius of curvature, and if the curve exists intime-time space, the analogous quantity will be called the radius oftime). In this time-time space the curvature has a dimension of 1/Time.Also, shown in FIG. 2 are some example sample points 230 at whichcurvatures are computed.

Referring now to FIGS. 3A and 3B, there is shown one example embodimentof extracting and aligning 300 sets of features associated withrespective detected complexes. FIG. 3A, illustrates continuouslyextracting features 320 from the computed curvature versus time signal200 shown in FIG. 2. Also shown in this example embodiment, are somesample times in milliseconds 330 when the extracted features occur onthe time line 130. FIG. 3A is also on the same time as that of FIGS. 1and 2. In the example embodiment shown in FIG. 3A, the sample times(where the curvatures are computed) have a fixed interval of 2milliseconds. Also, in this example embodiment, the beat associated withfirst complex 110 is detected at 11 milliseconds (t₁) and the beatassociated with the second subsequent complex 120 is detected at 101milliseconds (t₂).

FIG. 3B shows separating sets of extracted features 310 associated withthe complexes 110, 120 . . . on a continuous basis from the continuouslyextracted features 320 shown in FIG. 3A. In the example embodiment shownin FIG. 3B, a set of features 340 associated with first complex isseparated upon detecting the second subsequent complex 120. Also shownare separating a set of features 350 associated with second complex upondetecting a third subsequent complex. This process of separating a setof features associated with a complex is done on a continuous basis.Features associated with first complex is separated from the features ofsubsequent second complex in order to (so that the features are notmingled with the extracted features of the second subsequent complex)classify the sensed first complex 110. The next step is to identifyfiducial features 360, 370, and so on. In the example embodiment shownin FIG. 3B, fiducial feature 360 is identified based on comparing timeswhen the separated sets of extracted features associated with the firstcomplex 110 occur with a time when the heart beat associated with thefirst complex 110 is detected (‘t₁’), and choosing a feature from theset of features 340 that is closest in time to the time when the heartbeat associated with the first complex 110 is detected. In the exampleshown in FIG. 1, the first complex 110 is detected at 11 milliseconds.The times when the separated set of features 340 associated with thefirst complex 110 occur are 2, 4, 8, 10, 12, 14, 20, and 24 millisecondsas shown in FIG. 3A. Based on comparing the times when the separated setof features 340 occur with the time when first complex 110 was detected,the feature 360 that occurs at a time of 10 milliseconds is the closestin time to the detected first complex 110 which occurs at 11milliseconds. Therefore, extracted feature 360 is selected as thefiducial feature from the separated set of features 340 associated withthe first complex 110. Similarly, the extracted feature 370 is selectedas the fiducial feature for the separated set of features 350 associatedwith subsequent second complex 120. This process repeats itselfcontinuously on a real time basis. In some embodiments, the fiducialfeature is identified from the set of separated features associated withthe first complex based on a predetermined deviation value. Thepredetermined deviation value is based on a sample point having anamplitude farthest from a predetermined reference point.

The next step is to align the separated set of features 340 associatedwith the first complex 110, and to classify the separated set offeatures 340 by comparing the aligned set of features with a set oftemplates. Separated sets of features 340 and 350 are aligned tonormalize the separated sets of feature 340 and 350 so that the alignedfeatures aid in comparing the separated sets of features with a templateto classify the sensed complexes 110 and 120. FIG. 3B, illustrates oneexample embodiment of aligning the set of extracted features 340(associated with first complex 110) with respect to an identifiedfiducial feature 360. In this example embodiment, the time when theidentified fiducial feature occurs (at 10 milliseconds) is subtractedfrom each of the times when the separated set of features 340 associatedwith the first complex 110 occurs (at 2, 4, 8, 10, . . . milliseconds)to arrive at aligned times −8, −6, −2, 0, 2, 4, 10, and 14 millisecondsfor the separated set of features 340. Similarly, the aligned times −8,−2, 0, 2, 4, and 10 are computed for the separated set of features 350associated with the second complex 120. This process of aligningseparated set of features continues on a real time basis for thedetected complexes on the sensed cardiac signal 100. It can be seen fromthe above illustrated computation, that the process aligning helpsnormalize the times associated with each of the separated sets offeatures, and how it aids in comparing the separated set of features 340and 350 with a set of templates and in classifying the sensed complexes110 and 120.

FIG. 4, illustrates a method 400 of identifying and classifying a sensedcardiac signal on a real-time basis. Method 400, as shown in FIG. 4,begins with step 410 by sensing a cardiac signal 410 from the one ormore electrodes disposed in or around a heart 515 on a continuous basis.

The next step 420 in the process comprises computing curvatures atsample points X₁, X₂, X₃, . . . X_(I) on the sensed cardiac signal. Insome embodiments, the sample points X₁, X₂, X₃, . . . X_(I) are fixedintervals of time at which the sensed cardiac signal is computed for itscurvature. The sensed cardiac signal can be an atrial or ventricularsignal of a heart. In one embodiment, the curvatures at sample point X₁,X₂, X₃, . . . X_(I) is computed by fitting a cubic least square errorcurve to the sensed cardiac signal by using N sample points. In thisembodiment, N is an odd number greater than or equal to 5. Also in thisembodiment, the cardiac rhythm management system computes the curvatureat a mid point of the N sample points. In one embodiment, the cardiacrhythm management system computes curvature (K) at a sample point X₁ onthe cardiac signal by using 5 sample points over which the cubic fit is(N being 5 in this example) made using

K=(2C _(I)/(1+B _(I) ²)^(3/2))

where

B _(I) =T ₁ Y(I−2)+T ₂ Y(I−1)+T ₃ Y(I)+.T ₄ Y(I+1)+T ₅ Y(I+2)

C _(I) =S ₁ Y(I−2)+S ₂ Y(I−2)+S ₃ Y(I)+S ₄ Y(I+1)+S ₅ Y(I+2)

where S₁, S₂, S₃, . . . and T₁, T₂, T₃, . . . are constant terms in thesummation. In this embodiment, I corresponds to the sample point atwhich the curvature is being computed. For example, if the sample pointis X₃, then I will be equal to 3 and so on.

In another embodiment, the system computes average curvature between twoadjacent sample points by using a linear interpolation of the B_(I). andC_(I) between the two adjacent sample points (this is done by doing theintegral of the curvature between the two adjacent sample points). Thisintegration helps capture any curvatures that become larger between thetwo adjacent sample points, so that the point-wise curvature values maymore robustly represent the curvatures in the sensed cardiac signal. Thesystem also integrates curvature versus time curve to find the areaunder the curve. This area under the curve represent the total change inangle between two points on the original cardiac signal. Thus, peaks,turns, inflections, and the like in the cardiac signals can beidentified when the curvature versus time curve has peaks or nadirs.These peaks and nadirs denote points along the sensed cardiac signalwhere the turns are locally sharpest. Also, slow but steady turns on thesensed cardiac signal can be characterized by a small peak having asubstantial area under the curvature curve. Thus, integrating curvaturesbetween two adjacent sample points can help capture any curvatures thatmay become larger between any two adjacent sample points.

In one embodiment, the cardiac rhythm management system computescurvatures at sample points X₁, X₂, X₃, . . . X_(I) on the sensedcardiac signal on a continuous basis.

Then the next step 430 in the process includes extracting features fromthe computed curvatures by comparing the computed curvatures with a setof predetermined threshold values. FIG. 3A, illustrates extractingfeatures from the computed curvatures at the sample points X₁, X₂, X₃, .. . X_(I). In some embodiments, the set of predetermined thresholdvalues are based on a previous curvature value, a first curvature value,and a curvature threshold limit. In one embodiment, the step ofextracting features from the curvature versus time signal is done on acontinuous basis on the sensed cardiac signal as shown in FIG. 3A. Insome embodiments, the features are defined by one or more metrics. Inthis embodiment, the one or more metrics are defined as area under acomputed curvature, a time of centroid of the area, and a value oforiginal signal amplitude at a time of centroid of the area associatedwith the feature.

Then the next step 440 comprises separating a set of features associatedwith a first complex upon detecting a second subsequent complex from thesensed cardiac signal.

Separating of extracted features associated with the detected complex isillustrated in detail FIGS. 1, 2, 3A and 3B. In some embodiments, thesecond subsequent complex is detected based on continuously processingthe sensed cardiac signal to produce a signal that is the absolute valueof the first derivative of a sensed cardiac signal versus time. Thencomparing the produced absolute value versus time signal with apredetermined decaying threshold value to detect an occurrence of thesecond subsequent complex. In some other embodiments, the secondsubsequent complex is detected when the absolute value of the firstderivative exceeds a predetermined decaying threshold value. In someembodiments, the features associated with the first complex are storedin a memory until the second subsequent complex is detected, and at thatpoint, the features associated with the first complex are replaced bythe features associated with the second subsequent complex in thememory. The step of separating can comprise separating the set offeatures based on selecting features associated with the first complexhaving a predetermined time earlier than the detected second subsequentcomplex. In some embodiments, each of the set of features are furthercompared to an area threshold value to eliminate features less than thearea threshold value. This can result in a set of revised features thatcan represent the first complex.

Then the next step 450 in the process is to identify a fiducial featurefrom the set of separated features associated with the first complexbased on a predetermined deviation value. In some embodiments, fiducialfeature is identified based on comparing the time when the first complexoccurs with the times when the separated set of features associated withthe first complex occur. The process of identifying a fiducial featureis discussed in more detail with respect to FIGS. 3A and 3B.

Then the next step 460 in the process comprises aligning the set ofseparated features with respect to the identified fiducial feature. Theprocess of aligning is also discussed in more detail in FIG. 3B. Theprocess of aligning helps normalize the times associated with each ofthe separated set of features and further facilitates in comparing theseparated set of features with a set of predetermined templates toclassify the detected complex.

Then the next step 470 comprises comparing the aligned set of separatedfeatures with a set of predetermined templates. In one embodiment, theset of predetermined templates are one or more known types of heart beatsignals. In another embodiment, the set of predetermined templatesconsists of sets of template boxes or zones for each of the known typesof heart beat signals. Generally, each template box or zone can havecenter amplitude, a time width, and an amplitude width. Also generally,the template boxes or zones for each known type of heart beat canconsist of one or more template boxes or zones each of which can havedifferent centers and widths. In this embodiment, the comparing stepcomprises assigning a score based on how well each feature matches witheach of the set of template boxes or zones. In this embodiment, if theseparated set of features associated with the first complex fails tomatch with the set of templates then the complex is assigned with anunknown heart beat classification. In one embodiment, the set oftemplate boxes or zones are modified over time to reflect any changes insets of separated features used in matching with the template boxes orzones. This can permit known beat type templates to follow slow trendsor long-term changes in the cardiac signal.

The next step 480 in the process comprises classifying the detectedfirst complex based on the outcome of the comparison. Then the next step490 comprises providing a therapy based on the outcome of theclassification. In one embodiment, the next step includes guiding atherapy to a heart based on the outcome of the classification. Inanother embodiment, the next step in the process includes storingclassifications for future diagnostic purposes.

FIG. 5, is a schematic/block diagram 500 illustrating one embodiment ofportions of a cardiac rhythm management system 505, which is coupled toa heart 515. Cardiac rhythm management system 505 includes a powersource 580, a controller 540, a sensing circuit 520, a therapy circuit590, and a via node/bus 530. The sensing circuit 520 is coupled by alead 510 to the heart 515 for receiving, sensing, and or detectingelectrical heart signals. The sensing circuit 520 provides one or moresensed cardiac signals to the controller 540, via the node/bus 530. Thecontroller 540 also controls the delivery of a therapy provided by thetherapy circuit 590 and/or other circuits, as discussed below.

The controller 540 includes various modules, which are implementedeither in hardware or as one or more sequences of steps carried out on amicroprocessor or other controller. Such modules are illustratedseparately for conceptual clarity; it is understood that the variousmodules of the controller 540 need not be separately embodied, but maybe combined and/or otherwise implemented, such as in software/firmware.

In general terms, the sensing circuit 520 senses a cardiac signal on acontinuous basis from a heart tissue in contact with a catheter lead 510to which the sensing circuit 520 is coupled. In one embodiment, thesensed cardiac signal is an atrial signal. In another embodiment, thesensed cardiac signal is a ventricular signal. Sensed cardiac signalfrom the sensing circuit 520 is then received and processed by ananalyzer 550 of the controller 540 to compute curvatures continuously ona real-time basis at sample point X₁, X₂, X₃, . . . X_(I) on the sensedcardiac signal. In some embodiments, the sample points are spaced atfixed intervals of time preset by the analyzer 550 on the sensed cardiacsignal. In one embodiment, the analyzer 550 computes curvature at samplepoints X₁, X₂, X₃, . . . X_(I) by fitting a cubic least square errorcurve to the sensed cardiac signal using N sample points to fit thecubic least square error curve. In one embodiment, the analyzer 550computes the computing curvature (K) at a sample point X_(I) on thecardiac signal when using 5 sample points over which the cubic fit is (Nbeing 5 in this example) made using

K=(2C _(I)/(1+B _(I) ²)^(3/2))

where

B _(I) =T ₁ Y(I−2)+T ₂ Y(I−1)+T ₃ Y(I)+T ₄ Y(I+1)+T ₅ Y(I+2)

C _(I) =S ₁ Y(I−2)+S ₂ Y(I−2)+S ₃ Y(I)+S ₄ Y(I+1)+S ₅ Y(I+2)

where S₁, S₂, S₃. . . and T₁, T₂, T₃, . . . are constant terms in thesummation. In this example embodiment, I is equal to the point at whichthe curvature is being computed. For example, if the curvature is beingcomputed at sample point X₃, then I will be equal to 3 and so on. In oneembodiment, N is an odd number greater than or equal to 5. In thisembodiment, the analyzer 550 computes curvature at a midpoint of the Nnumber of sample points.

In another embodiment, the analyzer 550 computes curvature based on anaverage curvature between two adjacent sample points based on a linearinterpolation of the B_(I) and C_(I) between two adjacent points(integrating the computed curvatures between the two adjacent samplepoints). In one embodiment, the analyzer 550 computes the curvaturescontinuously on a real-time basis from the sensed cardiac signal. Thenthe analyzer 550 extracts features by comparing the computed curvaturesto a set of predetermined threshold values. In one embodiment, thepredetermined threshold values are based on a previous curvature value,a curvature value, and a curvature threshold limit. In one embodiment,the analyzer 550 extracts features on a continuous basis. Then theanalyzer 550 separates a set of features associated with a first complexupon detecting a second subsequent complex from the sensed cardiacsignal. Then the analyzer 550 identifies a fiducial feature from the setof separated features associated with the first complex based on apredetermined deviation value. Then the analyzer 550 further aligns theset of separated features with respect to the identified fiducialfeature. In one embodiment, the analyzer 550 separates the set offeatures associated with the first complex based on a predetermined timeearlier than the detected second subsequent complex. In one embodiment,the predetermined deviation value is based on a sample point having anamplitude farthest from a predetermined reference point.

Then a comparator 570 coupled to the analyzer 550, compares the alignedset of features associated with the first complex with a predeterminedset of templates, and classifies the first complex based on the outcomeof the comparison. In one embodiment, the comparator 570 issues acommand signal based on the outcome of the classification. In anotherembodiment, the therapy circuit 590 coupled to the comparator 570delivers the electrical energy through the lead 510 to at least oneelectrode disposed in or around the heart 515 upon receiving the commandsignal from the comparator 570. The electrical energy delivered by thetherapy circuit 590 can be a pacing pulse electrical energy. In oneembodiment, the analyzer 550 includes a variable gain circuitry that canadapt according to the changes in the sensed cardiac signal. In oneembodiment, the controller 540 includes a memory 560 coupled to thecomparator 570 and the analyzer 550 to store the extracted features ofthe first complex. In another embodiment, the memory 560 stores theclassified first complexes for diagnostic purposes.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the invention should, therefore, be determinedwith reference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

CONCLUSION

The above described method provides, among other things, a curvaturebased complex identification and classification. The process comprisessensing a cardiac signal and computing curvatures at sample points onthe sensed cardiac signal, and then to extract features from thecomputed curvatures. Then to compare the extracted features with a setof predetermined templates, and then to classify the cardiac signalbased on the outcome of the comparison.

What is claimed is:
 1. A method, comprising: sensing a cardiac signal;computing curvatures at sample points X₁, X₂, X₃, . . . X_(I) on thesensed cardiac signal; extracting features from the computed curvatures;comparing the extracted features with a set of predetermined templates;and classifying the cardiac signal based on an outcome of thecomparison.
 2. The method of claim 1, wherein the cardiac signalcomprises sensing complexes in real time, wherein the complexes arecardiac cycles.
 3. The method of claim 2, wherein computing thecurvatures at sample points X₁, X₂, X₃, . . . X_(I) comprises computingcurvatures at the sample points X₁, X₂, X₃, . . . X_(I) by fitting acubic least square error curve to sensed complexes on the cardiacsignal, wherein the sample points have a predetermined interval of timebetween adjacent sample points.
 4. The method of claim 3, whereincomputing the curvatures at sample points X₁, X₂, X₃, . . . X_(I)comprises fitting a cubic least square error curve around a sample pointby using N number of sample points to fit the cubic least square errorcurve, wherein N is an odd number greater than or equal to
 5. 5. Themethod of claim 4, wherein computing the curvature at the sample pointusing the N number of sample points comprises using the sample point asa mid point of the N number of sample points.
 6. The method of claim 5,wherein computing the curvature (K) at a sample point X_(I) of thecardiac signal when using 5 sample points to fit the cubic least squareerror curve, is based on  K 32 (2C _(I)/(1+B _(I) ²)^(3/2)) where B _(I)=T ₁ Y(I−2)+T ₂ Y(I−1)+T ₃ Y(I)+.T ₄ Y(I+1)+T ₅ Y(I+2) C _(I) =S ₁Y(I−2)+S ₂ Y(I−2)+S ₃ Y(I)+S ₄ Y(I+1)+S ₅ Y(I+2) where S and T areconstants.
 7. The method of claim 6, wherein computing the curvaturecomprises computing an average curvature between two adjacent samplepoints based on a linear interpolation of the B_(I) and C_(I) betweentwo adjacent points and integrating the computed curvatures between thetwo adjacent sample points X₁, X₂, X₃, . . . X_(I).
 8. The method ofclaim 3, wherein extracting the features further comprises: extractingfeatures from the computed curvatures is based on comparing the computedcurvatures to a set of predetermined threshold values; and identifyingand separating a set of extracted features associated with the firstcomplex upon detecting a second subsequent complex from the sensedcardiac signal.
 9. The method of claim 8, wherein the set ofpredetermined threshold values are based on a previous curvature value,a first curvature value, and a curvature threshold limit.
 10. The methodof claim 9, wherein separating the set of extracted features comprisesseparating the set of features based on identifying features associatedwith the first complex having a predetermined time earlier than thedetected second subsequent complex.
 11. The method of claim 8, whereincomparing the extracted features comprises comparing the separated setof extracted features associated with the first complex with a set ofpredetermined templates.
 12. The method of claim 11, wherein comparingthe extracted features further comprises: identifying a fiducial featurefrom the set of separated features associated with the first complexbased on a predetermined deviation value; and aligning the set ofseparated features associated using the identified fiducial feature. 13.The method of claim 12, wherein the predetermined deviation value isbased on a sample point having an amplitude farthest from apredetermined reference point.
 14. The method of claim 13, furtherincludes repeating the above steps for a real-time classification ofheat beat signals from the sensed cardiac signal.
 15. The method ofclaim 13, wherein the feature is defined by one or more metrics.
 16. Themethod of claim 15, wherein the one or more metrics are area under acomputed curvature, a time of centroid of the area, and a value oforiginal signal amplitude at a time of the centroid of the area.
 17. Themethod of claim 13, wherein comparing the set of features comprisescomparing the set of features associated with the first complex with oneor more predetermined heart beat signals.
 18. The method of claim 8,wherein the predetermined set of templates comprises one or morepredetermined template zones defined by a center time, a centeramplitude, a time width, and an amplitude width.
 19. The method of claim1, further comprises providing a therapy to a heart based on the outcomeof the classification.
 20. The method of claim 1, further comprisesguiding a therapy to a heart based on the outcome of the classification.21. The method of claim 1, further comprises storing classifications fordiagnostic purposes.
 22. The method of claim 1, wherein computing thecurvature further comprises permitting the computed curvature signal tohave a variable gain that adapts according to the changes in sensedcardiac signal.
 23. A cardiac rhythm management system, comprising: atleast one electrode; a signal sensing circuit coupled to the electrodeto sense a cardiac signal; a controller coupled to the sensing circuit,wherein the controller receives the sensed cardiac signal, and whereinthe controller includes: an analyzer, to compute curvatures at samplepoints X₁, X₂, X₃, . . . X_(I) on the sensed cardiac signal, wherein theanalyzer extracts features by comparing the computed curvatures to a setof predetermined threshold values; and a comparator, coupled to theanalyzer, compares the extracted features with a set of predeterminedtemplates, and classifies the sensed cardiac signal based on the outcomeof the comparison.
 24. The system of claim 23, wherein the sensing thecardiac signal includes sensing complexes on a real-time basis, whereinthe complexes comprise heart beat signals.
 25. The system of claim 23,wherein computing curvatures comprises computing curvatures at thesample points X₁, X₂, X₃, . . . X_(I) by fitting a cubic least squareerror curve to a first complex of the sensed cardiac signal using an Nsample points to fit the cubic least square error curve, wherein theanalyzer extracts features of the first complex by comparing thecomputed curvatures of the first complex to a set of predeterminedthreshold values, and wherein the analyzer further separates a set offeatures associated with the first complex upon detecting a secondsubsequent complex from the sensed cardiac signal; and wherein thecomparator compares the aligned set of features associated with thesecond subsequent complex with a set of predetermined templates, andclassifies the first complex based on the outcome of the comparison. 26.The system of claim 25, wherein the comparator issues a command signalbased on the outcome of the classification.
 27. The system of claim 26,further comprises a therapy circuit, coupled to the comparator, todeliver electrical energy through the at least one electrode uponreceiving the command signal from the comparator.
 28. The system ofclaim 27, wherein the electrical energy is a pacing pulse electricalenergy.
 29. The system of claim 25, wherein the set of predeterminedthreshold values are based on a previous curvature value, a curvaturevalue, and a curvature threshold limit.
 30. The system of claim 25,wherein the analyzer further identifies a fiducial feature from the setof separated features associated with the first complex based on apredetermined deviation value, and further aligns the set of separatedfeatures associated with the first complex with respect to theidentified fiducial feature.
 31. The system of claim 30, wherein theanalyzer separates the set of features associated with a first complexbased on a predetermined time earlier than the detected secondsubsequent complex.
 32. The system of claim 31, wherein thepredetermined deviation value is based on a sample point having anamplitude farthest from a predetermined reference point.
 33. The systemof claim 25, wherein N is an odd number greater than or equal to
 5. 34.The system of claim 33, wherein the computing curvature comprisescomputing curvature at a mid point of the N number of sample points. 35.The system of claim 34, wherein the analyzer computes the curvature (K)at a sample point X_(I) on the cardiac signal when using 5 sample pointsto fit the cubic least square error curve, based on K=(2C _(I)/(1+B _(I)²)^(3/2)) where B _(I) =T ₁ Y(I−2)+T ₂ Y(I−1)+T ₃ Y(I)+.T ₄ Y(I+1)+T ₅Y(I+2) C _(I) =S ₁ Y(I−2)+S ₂ Y(I−2)+S ₃ Y(I)+S ₄ Y(I+1)+S ₅ Y(I+2)where S and T are constants.
 36. The system of claim 35, wherein theanalyzer computes an average curvature between two adjacent samplepoints based on linear interpolation of the B_(I) and C_(I) between twoadjacent points and integrating the computed curvatures between the twoadjacent sample points.
 37. The system of claim 25, wherein the analyzerfurther comprises a variable gain, wherein the variable gain adaptsaccording to changes in the sensed cardiac signal.
 38. The system ofclaim 25, wherein the at least one electrode is disposed in or around aheart.
 39. The system of claim 25, further comprises a memory to storethe extracted features of the first complex.
 40. The system of claim 39,wherein the memory further stores the classified first complexes fordiagnostic purposes.